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Adversarial Examples for Model Robustness
Adversarial examples are subtly altered inputs (like images with imperceptible noise) that cause a machine learning model to misclassify them with high confidence. These examples exploit vulnerabilities in a model's decision boundaries. Understanding how to generate and defend against them is crucial for building robust AI systems.
In plain terms
Imagine a magician making a tiny, unnoticeable change to a card that tricks a seasoned card player into misidentifying it.
Why it matters
Identifying and mitigating adversarial attacks is vital for deploying AI in sensitive applications like self-driving cars or medical diagnosis, where robustness is paramount.
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